id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
32,891 | import warnings
from collections import OrderedDict
from typing import Optional, Union, cast
import torch
import torch.nn as nn
from torch import Tensor
from torch.nn.modules import Conv2d, Module
The provided code snippet includes necessary dependencies for implementing the `reinit_initial_conv_layer` function. Write... | Clones a Conv2d layer while optionally retaining some of the original weights. When replacing the first convolutional layer in a model with one that operates over different number of input channels, we sometimes want to keep a subset of the kernel weights the same (e.g. the RGB weights of an ImageNet pretrained model).... |
32,892 | import os
from typing import Any, Optional, Union
import timm
import torch
import torch.nn as nn
import torch.nn.functional as F
from kornia import augmentation as K
from torch import Tensor
from torchvision.models._api import WeightsEnum
from ..models import get_weight
from . import utils
from .base import BaseTask
T... | Computes the normalized mean squared error between x and y. Args: x: tensor x y: tensor y Returns: the normalized MSE between x and y |
32,893 | import os
import warnings
from collections.abc import Sequence
from typing import Any, Optional, Union
import kornia.augmentation as K
import lightning
import timm
import torch
import torch.nn as nn
import torch.nn.functional as F
from lightly.loss import NTXentLoss
from lightly.models.modules import MoCoProjectionHead... | Data augmentations used by MoCo. Args: version: Version of MoCo. size: Size of patch to crop. weights: Weight vector for grayscale computation. Returns: Data augmentation pipelines. |
32,894 | from __future__ import annotations
import bz2
import collections
import contextlib
import gzip
import lzma
import os
import sys
import tarfile
from collections.abc import Iterable, Iterator, Sequence
from dataclasses import dataclass
from datetime import datetime, timedelta
from typing import Any, cast, overload
import... | Download and extract an archive. Args: url: URL to download download_root: directory to download to extract_root: directory to extract to (defaults to ``download_root``) filename: download filename (defaults to basename of ``url``) md5: checksum for download verification |
32,895 | from __future__ import annotations
import bz2
import collections
import contextlib
import gzip
import lzma
import os
import sys
import tarfile
from collections.abc import Iterable, Iterator, Sequence
from dataclasses import dataclass
from datetime import datetime, timedelta
from typing import Any, cast, overload
import... | Download a dataset from Radiant Earth. Args: dataset_id: the ID of the dataset to fetch download_root: directory to download to api_key: the API key to use for all requests from the session. Can also be passed in via the ``MLHUB_API_KEY`` environment variable, or configured in ``~/.mlhub/profiles``. |
32,896 | from __future__ import annotations
import bz2
import collections
import contextlib
import gzip
import lzma
import os
import sys
import tarfile
from collections.abc import Iterable, Iterator, Sequence
from dataclasses import dataclass
from datetime import datetime, timedelta
from typing import Any, cast, overload
import... | Download a collection from Radiant Earth. Args: collection_id: the ID of the collection to fetch download_root: directory to download to api_key: the API key to use for all requests from the session. Can also be passed in via the ``MLHUB_API_KEY`` environment variable, or configured in ``~/.mlhub/profiles``. |
32,897 | from __future__ import annotations
import bz2
import collections
import contextlib
import gzip
import lzma
import os
import sys
import tarfile
from collections.abc import Iterable, Iterator, Sequence
from dataclasses import dataclass
from datetime import datetime, timedelta
from typing import Any, cast, overload
import... | Disambiguate partial timestamps. TorchGeo stores the timestamp of each file in a spatiotemporal R-tree. If the full timestamp isn't known, a file could represent a range of time. For example, in the CDL dataset, each mask spans an entire year. This method returns the maximum possible range of timestamps that ``date_str... |
32,898 | from __future__ import annotations
import bz2
import collections
import contextlib
import gzip
import lzma
import os
import sys
import tarfile
from collections.abc import Iterable, Iterator, Sequence
from dataclasses import dataclass
from datetime import datetime, timedelta
from typing import Any, cast, overload
import... | Context manager for changing directories. Args: dirname: directory to temporarily change to create: if True, create the destination directory |
32,899 | from __future__ import annotations
import bz2
import collections
import contextlib
import gzip
import lzma
import os
import sys
import tarfile
from collections.abc import Iterable, Iterator, Sequence
from dataclasses import dataclass
from datetime import datetime, timedelta
from typing import Any, cast, overload
import... | Stack a list of samples along a new axis. Useful for forming a mini-batch of samples to pass to :class:`torch.utils.data.DataLoader`. Args: samples: list of samples Returns: a single sample .. versionadded:: 0.2 |
32,900 | from __future__ import annotations
import bz2
import collections
import contextlib
import gzip
import lzma
import os
import sys
import tarfile
from collections.abc import Iterable, Iterator, Sequence
from dataclasses import dataclass
from datetime import datetime, timedelta
from typing import Any, cast, overload
import... | Concatenate a list of samples along an existing axis. Useful for joining samples in a :class:`torchgeo.datasets.IntersectionDataset`. Args: samples: list of samples Returns: a single sample .. versionadded:: 0.2 |
32,901 | from __future__ import annotations
import bz2
import collections
import contextlib
import gzip
import lzma
import os
import sys
import tarfile
from collections.abc import Iterable, Iterator, Sequence
from dataclasses import dataclass
from datetime import datetime, timedelta
from typing import Any, cast, overload
import... | Merge a list of samples. Useful for joining samples in a :class:`torchgeo.datasets.UnionDataset`. Args: samples: list of samples Returns: a single sample .. versionadded:: 0.2 |
32,902 | from __future__ import annotations
import bz2
import collections
import contextlib
import gzip
import lzma
import os
import sys
import tarfile
from collections.abc import Iterable, Iterator, Sequence
from dataclasses import dataclass
from datetime import datetime, timedelta
from typing import Any, cast, overload
import... | Reverse of :func:`stack_samples`. Useful for turning a mini-batch of samples into a list of samples. These individual samples can then be plotted using a dataset's ``plot`` method. Args: sample: a mini-batch of samples Returns: list of samples .. versionadded:: 0.2 |
32,903 | from __future__ import annotations
import bz2
import collections
import contextlib
import gzip
import lzma
import os
import sys
import tarfile
from collections.abc import Iterable, Iterator, Sequence
from dataclasses import dataclass
from datetime import datetime, timedelta
from typing import Any, cast, overload
import... | Load an image file using rasterio. Args: path: path to the image to be loaded Returns: the image |
32,904 | from __future__ import annotations
import bz2
import collections
import contextlib
import gzip
import lzma
import os
import sys
import tarfile
from collections.abc import Iterable, Iterator, Sequence
from dataclasses import dataclass
from datetime import datetime, timedelta
from typing import Any, cast, overload
import... | Sort Sentinel-2 band files in the correct order. |
32,905 | from __future__ import annotations
import bz2
import collections
import contextlib
import gzip
import lzma
import os
import sys
import tarfile
from collections.abc import Iterable, Iterator, Sequence
from dataclasses import dataclass
from datetime import datetime, timedelta
from typing import Any, cast, overload
import... | Overlay a semantic segmentation mask onto an image. Args: image: tensor of shape (3, h, w) and dtype uint8 mask: tensor of shape (h, w) with pixel values representing the classes and dtype bool alpha: alpha blend factor colors: list of RGB int tuples, or color strings e.g. red, #FF00FF Returns: a version of ``image`` o... |
32,906 | from __future__ import annotations
import bz2
import collections
import contextlib
import gzip
import lzma
import os
import sys
import tarfile
from collections.abc import Iterable, Iterator, Sequence
from dataclasses import dataclass
from datetime import datetime, timedelta
from typing import Any, cast, overload
import... | Converts an RGB colormap mask to a integer mask. Args: rgb: array mask of coded with RGB tuples colors: list of RGB tuples to convert to integer indices Returns: integer array mask |
32,907 | from __future__ import annotations
import bz2
import collections
import contextlib
import gzip
import lzma
import os
import sys
import tarfile
from collections.abc import Iterable, Iterator, Sequence
from dataclasses import dataclass
from datetime import datetime, timedelta
from typing import Any, cast, overload
import... | Applies percentile normalization to an input image. Specifically, this will rescale the values in the input such that values <= the lower percentile value will be 0 and values >= the upper percentile value will be 1. Using the 2nd and 98th percentile usually results in good visualizations. Args: img: image to normalize... |
32,908 | from __future__ import annotations
import bz2
import collections
import contextlib
import gzip
import lzma
import os
import sys
import tarfile
from collections.abc import Iterable, Iterator, Sequence
from dataclasses import dataclass
from datetime import datetime, timedelta
from typing import Any, cast, overload
import... | Checks if the given path is pointing to a Virtual File System. .. note:: Does not check if the path exists, or if it is a dir or file. VSI can for instance be Cloud Storage Blobs or zip-archives. They will start with a prefix indicating this. For examples of these, see references for the two accepted syntaxes. * https:... |
32,909 | from __future__ import annotations
import bz2
import collections
import contextlib
import gzip
import lzma
import os
import sys
import tarfile
from collections.abc import Iterable, Iterator, Sequence
from dataclasses import dataclass
from datetime import datetime, timedelta
from typing import Any, cast, overload
import... | Converts a :class:`numpy.ndarray` to :class:`torch.Tensor`. :func:`torch.from_tensor` rejects numpy types like uint16 that are not supported in pytorch. This function instead casts uint16 and uint32 numpy arrays to an appropriate pytorch type without loss of precision. For example, a uint32 array becomes an int64 tenso... |
32,910 | from collections.abc import Sequence
from copy import deepcopy
from itertools import accumulate
from math import floor, isclose
from typing import Optional, Union, cast
from rtree.index import Index, Property
from torch import Generator, default_generator, randint, randperm
from ..datasets import GeoDataset
from .utils... | Split a GeoDataset randomly assigning its index's BoundingBoxes. This function will go through each BoundingBox in the GeoDataset's index and randomly assign it to new GeoDatasets. Args: dataset: dataset to be split lengths: lengths or fractions of splits to be produced generator: (optional) generator used for the rand... |
32,911 | from collections.abc import Sequence
from copy import deepcopy
from itertools import accumulate
from math import floor, isclose
from typing import Optional, Union, cast
from rtree.index import Index, Property
from torch import Generator, default_generator, randint, randperm
from ..datasets import GeoDataset
from .utils... | Split a GeoDataset randomly splitting its index's BoundingBoxes. This function will go through each BoundingBox in the GeoDataset's index, split it in a random direction and assign the resulting BoundingBoxes to new GeoDatasets. Args: dataset: dataset to be split fractions: fractions of splits to be produced generator:... |
32,912 | from collections.abc import Sequence
from copy import deepcopy
from itertools import accumulate
from math import floor, isclose
from typing import Optional, Union, cast
from rtree.index import Index, Property
from torch import Generator, default_generator, randint, randperm
from ..datasets import GeoDataset
from .utils... | Overlays a grid over a GeoDataset and randomly assigns cells to new GeoDatasets. This function will go through each BoundingBox in the GeoDataset's index, overlay a grid over it, and randomly assign each cell to new GeoDatasets. Args: dataset: dataset to be split fractions: fractions of splits to be produced grid_size:... |
32,913 | from collections.abc import Sequence
from copy import deepcopy
from itertools import accumulate
from math import floor, isclose
from typing import Optional, Union, cast
from rtree.index import Index, Property
from torch import Generator, default_generator, randint, randperm
from ..datasets import GeoDataset
from .utils... | Split a GeoDataset intersecting it with a ROI for each desired new GeoDataset. Args: dataset: dataset to be split rois: regions of interest of splits to be produced Returns A list of the subset datasets. .. versionadded:: 0.5 |
32,914 | from collections.abc import Sequence
from copy import deepcopy
from itertools import accumulate
from math import floor, isclose
from typing import Optional, Union, cast
from rtree.index import Index, Property
from torch import Generator, default_generator, randint, randperm
from ..datasets import GeoDataset
from .utils... | Split a GeoDataset on its time dimension to create non-overlapping GeoDatasets. Args: dataset: dataset to be split lengths: lengths, fractions or pairs of timestamps (start, end) of splits to be produced Returns A list of the subset datasets. .. versionadded:: 0.5 |
32,915 | import glob
import os
import sys
from datetime import datetime, timedelta
from typing import Any
import numpy as np
import pandas as pd
from rasterio.crs import CRS
from .geo import GeoDataset
from .utils import BoundingBox, DatasetNotFoundError
The provided code snippet includes necessary dependencies for implementin... | Disambiguate partial timestamps. Based on :func:`torchgeo.datasets.utils.disambiguate_timestamps`. Args: year: year, possibly nan month: month, possibly nan day: day, possibly nan Returns: minimum and maximum possible time range |
32,916 | import glob
import os
from typing import Any, Callable, Optional, cast
from xml.etree.ElementTree import Element, parse
import matplotlib.patches as patches
import matplotlib.pyplot as plt
import numpy as np
import torch
from matplotlib.figure import Figure
from PIL import Image
from torch import Tensor
from .geo impor... | Read a PASCAL VOC annotation file. Args: path: path to xml file Returns: dict of image filename, points, and class labels |
32,917 | import os
from typing import Any, Callable, Optional
import matplotlib.pyplot as plt
import numpy as np
import torch
from matplotlib import patches
from matplotlib.figure import Figure
from PIL import Image
from torch import Tensor
from .geo import NonGeoDataset
from .utils import (
DatasetNotFoundError,
check_... | Convert coco polygons to mask tensor. Args: segmentations (List[int]): polygon coordinates height (int): image height width (int): image width Returns: Tensor: Mask tensor |
32,918 | import glob
import os
from typing import Any, Callable, Optional
from xml.etree import ElementTree
import matplotlib.patches as patches
import matplotlib.pyplot as plt
import numpy as np
import torch
from matplotlib.figure import Figure
from PIL import Image
from torch import Tensor
from .geo import NonGeoDataset
from ... | Read a PASCAL VOC annotation file. Args: path: path to xml file Returns: dict of image filename, points, and class labels |
32,919 | from typing import Any, Callable, Union
import torch.nn as nn
from torchvision.models._api import WeightsEnum
from .resnet import ResNet18_Weights, ResNet50_Weights, resnet18, resnet50
from .swin import Swin_V2_B_Weights, swin_v2_b
from .vit import ViTSmall16_Weights, vit_small_patch16_224
_model = {
"resnet18": re... | Get an instantiated model from its name. .. versionadded:: 0.4 Args: name: Name of the model. *args: Additional arguments passed to the model builder method. **kwargs: Additional keyword arguments passed to the model builder method. Returns: An instantiated model. |
32,920 | from typing import Any, Callable, Union
import torch.nn as nn
from torchvision.models._api import WeightsEnum
from .resnet import ResNet18_Weights, ResNet50_Weights, resnet18, resnet50
from .swin import Swin_V2_B_Weights, swin_v2_b
from .vit import ViTSmall16_Weights, vit_small_patch16_224
_model_weights = {
resnet... | Get the weights enum class associated with a given model. .. versionadded:: 0.4 Args: name: Model builder function or the name under which it is registered. Returns: The weights enum class associated with the model. |
32,921 | from typing import Any, Callable, Union
import torch.nn as nn
from torchvision.models._api import WeightsEnum
from .resnet import ResNet18_Weights, ResNet50_Weights, resnet18, resnet50
from .swin import Swin_V2_B_Weights, swin_v2_b
from .vit import ViTSmall16_Weights, vit_small_patch16_224
The provided code snippet in... | Get the weights enum value by its full name. .. versionadded:: 0.4 Args: name: Name of the weight enum entry. Returns: The requested weight enum. |
32,922 | from typing import Any, Callable, Union
import torch.nn as nn
from torchvision.models._api import WeightsEnum
from .resnet import ResNet18_Weights, ResNet50_Weights, resnet18, resnet50
from .swin import Swin_V2_B_Weights, swin_v2_b
from .vit import ViTSmall16_Weights, vit_small_patch16_224
_model = {
"resnet18": re... | List the registered models. .. versionadded:: 0.4 Returns: A list of registered models. |
32,923 | from typing import Any, Optional
import kornia.augmentation as K
import timm
import torch
from timm.models.vision_transformer import VisionTransformer
from torchvision.models._api import Weights, WeightsEnum
from ..transforms import AugmentationSequential
class ViTSmall16_Weights(WeightsEnum): # type: ignore[misc]
... | Vision Transform (ViT) small patch size 16 model. If you use this model in your research, please cite the following paper: * https://arxiv.org/abs/2010.11929 .. versionadded:: 0.4 Args: weights: Pre-trained model weights to use. *args: Additional arguments to pass to :func:`timm.create_model`. **kwargs: Additional keyw... |
32,924 | from typing import Any, Optional
import kornia.augmentation as K
import torch
import torchvision
from kornia.contrib import Lambda
from torchvision.models import SwinTransformer
from torchvision.models._api import Weights, WeightsEnum
from ..transforms import AugmentationSequential
class Swin_V2_B_Weights(WeightsEnum):... | Swin Transformer v2 base model. If you use this model in your research, please cite the following paper: * https://arxiv.org/abs/2111.09883 .. versionadded:: 0.6 Args: weights: Pre-trained model weights to use. *args: Additional arguments to pass to :class:`torchvision.models.swin_transformer.SwinTransformer`. **kwargs... |
32,925 | from typing import Any, Optional
import kornia.augmentation as K
import timm
import torch
from timm.models import ResNet
from torchvision.models._api import Weights, WeightsEnum
from ..transforms import AugmentationSequential
class ResNet18_Weights(WeightsEnum): # type: ignore[misc]
"""ResNet18 weights.
For `t... | ResNet-18 model. If you use this model in your research, please cite the following paper: * https://arxiv.org/pdf/1512.03385.pdf .. versionadded:: 0.4 Args: weights: Pre-trained model weights to use. *args: Additional arguments to pass to :func:`timm.create_model` **kwargs: Additional keywork arguments to pass to :func... |
32,926 | from typing import Any, Optional
import kornia.augmentation as K
import timm
import torch
from timm.models import ResNet
from torchvision.models._api import Weights, WeightsEnum
from ..transforms import AugmentationSequential
class ResNet50_Weights(WeightsEnum): # type: ignore[misc]
"""ResNet50 weights.
For `t... | ResNet-50 model. If you use this model in your research, please cite the following paper: * https://arxiv.org/pdf/1512.03385.pdf .. versionchanged:: 0.4 Switched to multi-weight support API. Args: weights: Pre-trained model weights to use. *args: Additional arguments to pass to :func:`timm.create_model`. **kwargs: Addi... |
32,927 | import cv2
import einops
import numpy as np
import torch
import random
from pytorch_lightning import seed_everything
from cldm.model import create_model, load_state_dict
from cldm.ddim_hacked import DDIMSampler
from cldm.hack import disable_verbosity, enable_sliced_attention
from datasets.data_utils import *
import al... | null |
32,928 | import cv2
import einops
import numpy as np
import torch
import random
from pytorch_lightning import seed_everything
from cldm.model import create_model, load_state_dict
from cldm.ddim_hacked import DDIMSampler
from cldm.hack import disable_verbosity, enable_sliced_attention
from datasets.data_utils import *
cv2.setNu... | null |
32,929 | from pathlib import Path
import re
from typing import List, Tuple
from setuptools import setup, find_packages
HERE = Path(__file__).parent
try:
with open(HERE / "README.md", encoding="utf-8") as f:
long_description = "\n" + f.read()
except FileNotFoundError:
long_description = DESCRIPTION
requirements, ... | null |
32,930 | from pathlib import Path
import re
from typing import List, Tuple
from setuptools import setup, find_packages
HERE = Path(__file__).parent
try:
with open(HERE / "README.md", encoding="utf-8") as f:
long_description = "\n" + f.read()
except FileNotFoundError:
long_description = DESCRIPTION
def get_packa... | null |
32,931 | import torch
import torch.nn as nn
from torch.nn.init import trunc_normal_
from torch.nn.utils import weight_norm
def _build_mlp(nlayers, in_dim, bottleneck_dim, hidden_dim=None, use_bn=False, bias=True):
if nlayers == 1:
return nn.Linear(in_dim, bottleneck_dim, bias=bias)
else:
layers = [nn.Li... | null |
32,932 | from torch import nn
def drop_path(x, drop_prob: float = 0.0, training: bool = False):
if drop_prob == 0.0 or not training:
return x
keep_prob = 1 - drop_prob
shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
random_tensor = x.new_empty(shape).berno... | null |
32,933 | from typing import Callable, Optional, Tuple, Union
from torch import Tensor
import torch.nn as nn
def make_2tuple(x):
if isinstance(x, tuple):
assert len(x) == 2
return x
assert isinstance(x, int)
return (x, x) | null |
32,934 | import logging
from typing import Callable, List, Any, Tuple, Dict
import torch
from torch import nn, Tensor
from .attention import Attention, MemEffAttention
from .drop_path import DropPath
from .layer_scale import LayerScale
from .mlp import Mlp
def drop_add_residual_stochastic_depth(
x: Tensor,
residual_fun... | null |
32,935 | import logging
from typing import Callable, List, Any, Tuple, Dict
import torch
from torch import nn, Tensor
from .attention import Attention, MemEffAttention
from .drop_path import DropPath
from .layer_scale import LayerScale
from .mlp import Mlp
def get_branges_scales(x, sample_drop_ratio=0.0):
b, n, d = x.shape
... | null |
32,936 | import itertools
from typing import Any, Optional
import warnings
import numpy as np
import torch
from torch.utils.data.sampler import Sampler
import dinov2.distributed as distributed
def _get_torch_dtype(size: int) -> Any:
return torch.int32 if size <= 2**31 else torch.int64
The provided code snippet includes nec... | Generate the indices of a random permutation. |
32,937 | import itertools
from typing import Any, Optional
import warnings
import numpy as np
import torch
from torch.utils.data.sampler import Sampler
import dinov2.distributed as distributed
def _get_numpy_dtype(size: int) -> Any:
return np.int32 if size <= 2**31 else np.int64
def _shuffle_tensor_slice(
*, tensor: to... | null |
32,938 | import itertools
from typing import Any, Optional
import warnings
import numpy as np
import torch
from torch.utils.data.sampler import Sampler
import dinov2.distributed as distributed
def _new_shuffle_tensor_slice(
*, tensor: torch.Tensor, start: int = 0, step: int = 1, generator: torch.Generator
) -> np.ndarray:
... | null |
32,939 | import itertools
from typing import Any, Optional
import warnings
import numpy as np
import torch
from torch.utils.data.sampler import Sampler
import dinov2.distributed as distributed
def _make_seed(seed: int, start: int, iter_count: int) -> int:
# NOTE: Tried a few variants (including iter_count << 32), this one ... | null |
32,940 | from typing import Sequence
import torch
from torchvision import transforms
class MaybeToTensor(transforms.ToTensor):
"""
Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor, or keep as is if already a tensor.
"""
def __call__(self, pic):
"""
Args:
pic (PIL Image, numpy.nd... | null |
32,941 | from typing import Sequence
import torch
from torchvision import transforms
class MaybeToTensor(transforms.ToTensor):
"""
Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor, or keep as is if already a tensor.
"""
def __call__(self, pic):
"""
Args:
pic (PIL Image, numpy.nd... | null |
32,942 | import torch
import random
def collate_data_and_cast(samples_list, mask_ratio_tuple, mask_probability, dtype, n_tokens=None, mask_generator=None):
# dtype = torch.half # TODO: Remove
n_global_crops = len(samples_list[0][0]["global_crops"])
n_local_crops = len(samples_list[0][0]["local_crops"])
colla... | null |
32,943 | from dataclasses import dataclass
from enum import Enum
from functools import lru_cache
from gzip import GzipFile
from io import BytesIO
from mmap import ACCESS_READ, mmap
import os
from typing import Any, Callable, List, Optional, Set, Tuple
import warnings
import numpy as np
from .extended import ExtendedVisionDatase... | null |
32,944 | import logging
from enum import Enum
from typing import Any, Callable, List, Optional, TypeVar
import torch
from torch.utils.data import Sampler
from .datasets import ImageNet, ImageNet22k
from .samplers import EpochSampler, InfiniteSampler, ShardedInfiniteSampler
def _make_bool_str(b: bool) -> str:
return "yes" i... | null |
32,945 | import logging
from enum import Enum
from typing import Any, Callable, List, Optional, TypeVar
import torch
from torch.utils.data import Sampler
from .datasets import ImageNet, ImageNet22k
from .samplers import EpochSampler, InfiniteSampler, ShardedInfiniteSampler
def _make_sample_transform(image_transform: Optional[C... | null |
32,946 | import logging
from enum import Enum
from typing import Any, Callable, List, Optional, TypeVar
import torch
from torch.utils.data import Sampler
from .datasets import ImageNet, ImageNet22k
from .samplers import EpochSampler, InfiniteSampler, ShardedInfiniteSampler
logger = logging.getLogger("dinov2")
def _parse_dataset... | Creates a dataset with the specified parameters. Args: dataset_str: A dataset string description (e.g. ImageNet:split=TRAIN). transform: A transform to apply to images. target_transform: A transform to apply to targets. Returns: The created dataset. |
32,947 | import logging
from enum import Enum
from typing import Any, Callable, List, Optional, TypeVar
import torch
from torch.utils.data import Sampler
from .datasets import ImageNet, ImageNet22k
from .samplers import EpochSampler, InfiniteSampler, ShardedInfiniteSampler
logger = logging.getLogger("dinov2")
class SamplerType(... | Creates a data loader with the specified parameters. Args: dataset: A dataset (third party, LaViDa or WebDataset). batch_size: The size of batches to generate. num_workers: The number of workers to use. shuffle: Whether to shuffle samples. seed: The random seed to use. sampler_type: Which sampler to use: EPOCH, INFINIT... |
32,948 | import argparse
import logging
import math
import os
from functools import partial
from fvcore.common.checkpoint import PeriodicCheckpointer
import torch
from dinov2.data import SamplerType, make_data_loader, make_dataset
from dinov2.data import collate_data_and_cast, DataAugmentationDINO, MaskingGenerator
import dinov... | null |
32,949 | import argparse
import logging
import math
import os
from functools import partial
from fvcore.common.checkpoint import PeriodicCheckpointer
import torch
from dinov2.data import SamplerType, make_data_loader, make_dataset
from dinov2.data import collate_data_and_cast, DataAugmentationDINO, MaskingGenerator
import dinov... | null |
32,950 | import argparse
import logging
import os
from pathlib import Path
from typing import List, Optional
import submitit
from dinov2.utils.cluster import (
get_slurm_executor_parameters,
get_slurm_partition,
get_user_checkpoint_path,
)
def get_args_parser(
description: Optional[str] = None,
parents: Opt... | null |
32,951 | import argparse
import logging
import os
from pathlib import Path
from typing import List, Optional
import submitit
from dinov2.utils.cluster import (
get_slurm_executor_parameters,
get_slurm_partition,
get_user_checkpoint_path,
)
logger = logging.getLogger("dinov2")
def get_shared_folder() -> Path:
use... | null |
32,952 | import torch
import torch.distributed as dist
import torch.nn.functional as F
from torch import nn
import logging
def lossfunc(t, s, temp):
s = s.float()
t = t.float()
if s.ndim == 2:
return -cross_entropy(s.unsqueeze(0), t.unsqueeze(0), temp, bw_inplace=True).squeeze(0)
eli... | null |
32,953 | import torch
import torch.distributed as dist
import torch.nn.functional as F
from torch import nn
import logging
def lossfunc(t, s, temp):
return torch.sum(t * F.log_softmax(s / temp, dim=-1), dim=-1) | null |
32,954 | from functools import partial
import math
import logging
from typing import Sequence, Tuple, Union, Callable
import torch
import torch.nn as nn
import torch.utils.checkpoint
from torch.nn.init import trunc_normal_
from dinov2.layers import Mlp, PatchEmbed, SwiGLUFFNFused, MemEffAttention, NestedTensorBlock as Block
de... | null |
32,955 | from functools import partial
import math
import logging
from typing import Sequence, Tuple, Union, Callable
import torch
import torch.nn as nn
import torch.utils.checkpoint
from torch.nn.init import trunc_normal_
from dinov2.layers import Mlp, PatchEmbed, SwiGLUFFNFused, MemEffAttention, NestedTensorBlock as Block
Th... | ViT weight initialization, original timm impl (for reproducibility) |
32,956 | from functools import partial
import math
import logging
from typing import Sequence, Tuple, Union, Callable
import torch
import torch.nn as nn
import torch.utils.checkpoint
from torch.nn.init import trunc_normal_
from dinov2.layers import Mlp, PatchEmbed, SwiGLUFFNFused, MemEffAttention, NestedTensorBlock as Block
cla... | null |
32,957 | from functools import partial
import math
import logging
from typing import Sequence, Tuple, Union, Callable
import torch
import torch.nn as nn
import torch.utils.checkpoint
from torch.nn.init import trunc_normal_
from dinov2.layers import Mlp, PatchEmbed, SwiGLUFFNFused, MemEffAttention, NestedTensorBlock as Block
cla... | null |
32,958 | from functools import partial
import math
import logging
from typing import Sequence, Tuple, Union, Callable
import torch
import torch.nn as nn
import torch.utils.checkpoint
from torch.nn.init import trunc_normal_
from dinov2.layers import Mlp, PatchEmbed, SwiGLUFFNFused, MemEffAttention, NestedTensorBlock as Block
cla... | null |
32,959 | from functools import partial
import math
import logging
from typing import Sequence, Tuple, Union, Callable
import torch
import torch.nn as nn
import torch.utils.checkpoint
from torch.nn.init import trunc_normal_
from dinov2.layers import Mlp, PatchEmbed, SwiGLUFFNFused, MemEffAttention, NestedTensorBlock as Block
cla... | Close to ViT-giant, with embed-dim 1536 and 24 heads => embed-dim per head 64 |
32,960 | from collections import defaultdict
import logging
logger = logging.getLogger("dinov2")
def get_vit_lr_decay_rate(name, lr_decay_rate=1.0, num_layers=12, force_is_backbone=False, chunked_blocks=False):
"""
Calculate lr decay rate for different ViT blocks.
Args:
name (string): parameter name.
... | null |
32,961 | from collections import defaultdict
import logging
def fuse_params_groups(all_params_groups, keys=("lr_multiplier", "wd_multiplier", "is_last_layer")):
fused_params_groups = defaultdict(lambda: {"params": []})
for d in all_params_groups:
identifier = ""
for k in keys:
identifier += ... | null |
32,962 | import logging
import os
import random
import subprocess
from urllib.parse import urlparse
import numpy as np
import torch
from torch import nn
logger = logging.getLogger("dinov2")
def load_pretrained_weights(model, pretrained_weights, checkpoint_key):
if urlparse(pretrained_weights).scheme: # If it looks like an... | null |
32,963 | import logging
import os
import random
import subprocess
from urllib.parse import urlparse
import numpy as np
import torch
from torch import nn
The provided code snippet includes necessary dependencies for implementing the `fix_random_seeds` function. Write a Python function `def fix_random_seeds(seed=31)` to solve th... | Fix random seeds. |
32,964 | import logging
import os
import random
import subprocess
from urllib.parse import urlparse
import numpy as np
import torch
from torch import nn
def get_sha():
cwd = os.path.dirname(os.path.abspath(__file__))
def _run(command):
return subprocess.check_output(command, cwd=cwd).decode("ascii").strip()
... | null |
32,965 | import logging
import os
import random
import subprocess
from urllib.parse import urlparse
import numpy as np
import torch
from torch import nn
def has_batchnorms(model):
bn_types = (nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d, nn.SyncBatchNorm)
for name, module in model.named_modules():
if isinstan... | null |
32,966 | import math
import logging
import os
from omegaconf import OmegaConf
import dinov2.distributed as distributed
from dinov2.logging import setup_logging
from dinov2.utils import utils
from dinov2.configs import dinov2_default_config
def apply_scaling_rules_to_cfg(cfg): # to fix
if cfg.optim.scaling_rule == "sqrt_wrt... | Create configs and perform basic setups. |
32,967 | from enum import Enum
import os
from pathlib import Path
from typing import Any, Dict, Optional
class ClusterType(Enum):
def get_checkpoint_path(cluster_type: Optional[ClusterType] = None) -> Optional[Path]:
def get_user_checkpoint_path(cluster_type: Optional[ClusterType] = None) -> Optional[Path]:
checkpoint_path... | null |
32,968 | from enum import Enum
import os
from pathlib import Path
from typing import Any, Dict, Optional
class ClusterType(Enum):
def get_cluster_type(cluster_type: Optional[ClusterType] = None) -> Optional[ClusterType]:
def get_slurm_partition(cluster_type: Optional[ClusterType] = None) -> Optional[str]:
def get_slurm_executo... | null |
32,969 | from typing import Dict, Union
import numpy as np
import torch
TypeSpec = Union[str, np.dtype, torch.dtype]
_NUMPY_TO_TORCH_DTYPE: Dict[np.dtype, torch.dtype] = {
np.dtype("bool"): torch.bool,
np.dtype("uint8"): torch.uint8,
np.dtype("int8"): torch.int8,
np.dtype("int16"): torch.int16,
np.dtype("int... | null |
32,970 | import argparse
from typing import Any, List, Optional, Tuple
import torch
import torch.backends.cudnn as cudnn
from dinov2.models import build_model_from_cfg
from dinov2.utils.config import setup
import dinov2.utils.utils as dinov2_utils
def get_args_parser(
description: Optional[str] = None,
parents: Optiona... | null |
32,971 | import argparse
from typing import Any, List, Optional, Tuple
import torch
import torch.backends.cudnn as cudnn
from dinov2.models import build_model_from_cfg
from dinov2.utils.config import setup
import dinov2.utils.utils as dinov2_utils
def get_autocast_dtype(config):
teacher_dtype_str = config.compute_precision.... | null |
32,972 | import logging
from typing import Dict, Optional
import torch
from torch import nn
from torchmetrics import MetricCollection
from dinov2.data import DatasetWithEnumeratedTargets, SamplerType, make_data_loader
import dinov2.distributed as distributed
from dinov2.logging import MetricLogger
logger = logging.getLogger("di... | null |
32,973 | import logging
from typing import Dict, Optional
import torch
from torch import nn
from torchmetrics import MetricCollection
from dinov2.data import DatasetWithEnumeratedTargets, SamplerType, make_data_loader
import dinov2.distributed as distributed
from dinov2.logging import MetricLogger
def extract_features_with_data... | null |
32,974 | import argparse
import gc
import logging
import sys
import time
from typing import List, Optional
from cuml.linear_model import LogisticRegression
import torch
import torch.backends.cudnn as cudnn
import torch.distributed
from torch import nn
from torch.utils.data import TensorDataset
from torchmetrics import MetricTra... | null |
32,975 | import argparse
import gc
import logging
import sys
import time
from typing import List, Optional
from cuml.linear_model import LogisticRegression
import torch
import torch.backends.cudnn as cudnn
import torch.distributed
from torch import nn
from torch.utils.data import TensorDataset
from torchmetrics import MetricTra... | null |
32,976 | import argparse
from functools import partial
import json
import logging
import os
import sys
from typing import List, Optional
import torch
from torch.nn.functional import one_hot, softmax
import dinov2.distributed as distributed
from dinov2.data import SamplerType, make_data_loader, make_dataset
from dinov2.data.tran... | null |
32,977 | import argparse
from functools import partial
import json
import logging
import os
import sys
from typing import List, Optional
import torch
from torch.nn.functional import one_hot, softmax
import dinov2.distributed as distributed
from dinov2.data import SamplerType, make_data_loader, make_dataset
from dinov2.data.tran... | null |
32,978 | import argparse
from functools import partial
import json
import logging
import os
import sys
from typing import List, Optional
import numpy as np
import torch
import torch.nn as nn
from torch.nn.parallel import DistributedDataParallel
from fvcore.common.checkpoint import Checkpointer, PeriodicCheckpointer
from dinov2.... | null |
32,979 | import argparse
from functools import partial
import json
import logging
import os
import sys
from typing import List, Optional
import numpy as np
import torch
import torch.nn as nn
from torch.nn.parallel import DistributedDataParallel
from fvcore.common.checkpoint import Checkpointer, PeriodicCheckpointer
from dinov2.... | null |
32,980 | from enum import Enum
import logging
from typing import Any, Dict, Optional
import torch
from torch import Tensor
from torchmetrics import Metric, MetricCollection
from torchmetrics.classification import MulticlassAccuracy
from torchmetrics.utilities.data import dim_zero_cat, select_topk
class MetricType(Enum):
MEA... | null |
32,981 | import torch
import torch.nn as nn
def _make_dinov2_model(
*,
arch_name: str = "vit_large",
img_size: int = 518,
patch_size: int = 14,
init_values: float = 1.0,
ffn_layer: str = "mlp",
block_chunks: int = 0,
pretrained: bool = True,
**kwargs,
):
from dinov2.models import vision_t... | DINOv2 ViT-S/14 model (optionally) pretrained on the LVD-142M dataset. |
32,982 | import torch
import torch.nn as nn
def _make_dinov2_model(
*,
arch_name: str = "vit_large",
img_size: int = 518,
patch_size: int = 14,
init_values: float = 1.0,
ffn_layer: str = "mlp",
block_chunks: int = 0,
pretrained: bool = True,
**kwargs,
):
from dinov2.models import vision_t... | DINOv2 ViT-B/14 model pretrained on the LVD-142M dataset. |
32,983 | import torch
import torch.nn as nn
def _make_dinov2_model(
*,
arch_name: str = "vit_large",
img_size: int = 518,
patch_size: int = 14,
init_values: float = 1.0,
ffn_layer: str = "mlp",
block_chunks: int = 0,
pretrained: bool = True,
**kwargs,
):
from dinov2.models import vision_t... | DINOv2 ViT-L/14 model (optionally) pretrained on the LVD-142M dataset. |
32,984 | import torch
import torch.nn as nn
def _make_dinov2_model(
*,
arch_name: str = "vit_large",
img_size: int = 518,
patch_size: int = 14,
init_values: float = 1.0,
ffn_layer: str = "mlp",
block_chunks: int = 0,
pretrained: bool = True,
**kwargs,
):
from dinov2.models import vision_t... | DINOv2 ViT-g/14 model (optionally) pretrained on the LVD-142M dataset. |
32,985 | import torch
import torch.nn as nn
def _make_dinov2_linear_classifier(
*,
arch_name: str = "vit_large",
layers: int = 4,
pretrained: bool = True,
**kwargs,
):
backbone = _make_dinov2_model(arch_name=arch_name, pretrained=pretrained, **kwargs)
embed_dim = backbone.embed_dim
patch_size = b... | Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-S/14 backbone (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k. |
32,986 | import torch
import torch.nn as nn
def _make_dinov2_linear_classifier(
*,
arch_name: str = "vit_large",
layers: int = 4,
pretrained: bool = True,
**kwargs,
):
backbone = _make_dinov2_model(arch_name=arch_name, pretrained=pretrained, **kwargs)
embed_dim = backbone.embed_dim
patch_size = b... | Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-B/14 backbone (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k. |
32,987 | import torch
import torch.nn as nn
def _make_dinov2_linear_classifier(
*,
arch_name: str = "vit_large",
layers: int = 4,
pretrained: bool = True,
**kwargs,
):
backbone = _make_dinov2_model(arch_name=arch_name, pretrained=pretrained, **kwargs)
embed_dim = backbone.embed_dim
patch_size = b... | Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-L/14 backbone (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k. |
32,988 | import torch
import torch.nn as nn
def _make_dinov2_linear_classifier(
*,
arch_name: str = "vit_large",
layers: int = 4,
pretrained: bool = True,
**kwargs,
):
backbone = _make_dinov2_model(arch_name=arch_name, pretrained=pretrained, **kwargs)
embed_dim = backbone.embed_dim
patch_size = b... | Linear classifier (1 or 4 layers) on top of a DINOv2 ViT-g/14 backbone (optionally) pretrained on the LVD-142M dataset and trained on ImageNet-1k. |
32,989 | from cog import BasePredictor, Input, Path
import os
import cv2
import time
import torch
import einops
import random
import subprocess
import numpy as np
from cldm.ddim_hacked import DDIMSampler
from cldm.model import create_model, load_state_dict
from cldm.hack import disable_verbosity
from datasets.data_utils import ... | null |
32,990 | from cog import BasePredictor, Input, Path
import os
import cv2
import time
import torch
import einops
import random
import subprocess
import numpy as np
from cldm.ddim_hacked import DDIMSampler
from cldm.model import create_model, load_state_dict
from cldm.hack import disable_verbosity
from datasets.data_utils import ... | null |
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